OpenNN memory problem
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I use a modified version of airfoil_self_noise from the OpenNN (http://www.opennn.net/) library which is attached.I use Qt Creator 4.8.2 (community) to compile it using the kit : MSVC 2017 64 bit with Microsoft Visual C++ compiler 15.0 (x86_AMD64) & cdb debugger.Compilation ends successfully without problems in both cases(see in file :case1 & case2) but when I run the exe_case1 there is no problem while when I run the exe_case2 I obtain this error:
Error :OpenNN Exception: TrainingStrategy class.
LevenbergMarquardtAlgorithm* get_Levenberg_Marquardt_algorithm_pointer(void) const method.
Levenberg_Marquardt algorithm pointer is NULL.The only difference between the two is in the lines 293-309.[0_1552903063134_main.cpp](Uploading 100%)
I'm not a C++ expert so this problem is difficult for me. Can you help me?
Thanks!
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Hi and welcome to devnet,
Can you post the code ? Your upload didn't work.
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Here is my code:
// System includes #include <iostream> #include <fstream> #include <sstream> #include <string> #include <cstring> #include <time.h> // OpenNN includes #include "../../opennn/opennn.h" #include <levenberg_marquardt_algorithm.h> #include <quasi_newton_method.h> using namespace OpenNN; int main(void) { try { int rank = 0; #ifdef __OPENNN_MPI__ int size = 1; MPI_Init(NULL,NULL); MPI_Comm_size(MPI_COMM_WORLD, &size); MPI_Comm_rank(MPI_COMM_WORLD, &rank); #endif if(rank == 0) { std::cout << "OpenNN. Airfoil Self-Noise Application." << std::endl; } srand((unsigned)time(NULL)); // Global variables DataSet data_set; NeuralNetwork neural_network; LossIndex loss_index; TrainingStrategy training_strategy; // Local variables DataSet local_data_set; NeuralNetwork local_neural_network; LossIndex local_loss_index; TrainingStrategy local_training_strategy; if(rank == 0) { // Data set data_set.set_data_file_name("../data/temprenetinputs11.csv"); // controllare dati data_set.set_separator("Space"); data_set.load_data(); printf("%s\n","data_set"); std::cout << typeid(data_set).name() << std::endl; // Variables Variables* variables_pointer = data_set.get_variables_pointer(); variables_pointer->set_name(0, "1"); variables_pointer->set_units(0, "pip"); variables_pointer->set_use(0, Variables::Input); variables_pointer->set_name(1, "2"); variables_pointer->set_units(1, "pip"); variables_pointer->set_use(1, Variables::Input); variables_pointer->set_name(2, "3"); variables_pointer->set_units(2, "pip"); variables_pointer->set_use(2, Variables::Input); variables_pointer->set_name(3, "4"); variables_pointer->set_units(3, "pip"); variables_pointer->set_use(3, Variables::Input); variables_pointer->set_name(4, "5"); variables_pointer->set_units(4, "pip"); variables_pointer->set_use(4, Variables::Input); variables_pointer->set_name(5, "6"); variables_pointer->set_units(5, "pip"); variables_pointer->set_use(5, Variables::Input); variables_pointer->set_name(6, "7"); variables_pointer->set_units(6, "pip"); variables_pointer->set_use(6, Variables::Input); variables_pointer->set_name(7, "8"); variables_pointer->set_units(7, "pip"); variables_pointer->set_use(7, Variables::Input); variables_pointer->set_name(8, "9"); variables_pointer->set_units(8, "pip"); variables_pointer->set_use(8, Variables::Input); variables_pointer->set_name(9, "10"); variables_pointer->set_units(9, "pip"); variables_pointer->set_use(9, Variables::Input); variables_pointer->set_name(10, "11"); variables_pointer->set_units(10, "pip"); variables_pointer->set_use(10, Variables::Input); variables_pointer->set_name(11, "12"); variables_pointer->set_units(11, "pip"); variables_pointer->set_use(11, Variables::Input); variables_pointer->set_name(12, "13"); variables_pointer->set_units(12, "pip"); variables_pointer->set_use(12, Variables::Input); variables_pointer->set_name(13, "14"); variables_pointer->set_units(13, "pip"); variables_pointer->set_use(13, Variables::Input); variables_pointer->set_name(14, "15"); variables_pointer->set_units(14, "pip"); variables_pointer->set_use(14, Variables::Input); variables_pointer->set_name(15, "16"); variables_pointer->set_units(15, "pip"); variables_pointer->set_use(15, Variables::Input); variables_pointer->set_name(16, "17"); variables_pointer->set_units(16, "pip"); variables_pointer->set_use(16, Variables::Input); variables_pointer->set_name(17, "18"); variables_pointer->set_units(17, "pip"); variables_pointer->set_use(17, Variables::Input); variables_pointer->set_name(18, "19"); variables_pointer->set_units(18, "pip"); variables_pointer->set_use(18, Variables::Input); variables_pointer->set_name(19, "20"); variables_pointer->set_units(19, "pip"); variables_pointer->set_use(19, Variables::Input); variables_pointer->set_name(20, "21"); variables_pointer->set_units(20, "pip"); variables_pointer->set_use(20, Variables::Input); variables_pointer->set_name(21, "22"); variables_pointer->set_units(21, "pip"); variables_pointer->set_use(21, Variables::Input); variables_pointer->set_name(22, "23"); variables_pointer->set_units(22, "pip"); variables_pointer->set_use(22, Variables::Input); variables_pointer->set_name(23, "24"); variables_pointer->set_units(23, "pip"); variables_pointer->set_use(23, Variables::Input); variables_pointer->set_name(24, "25"); variables_pointer->set_units(24, "pip"); variables_pointer->set_use(24, Variables::Input); variables_pointer->set_name(25, "26"); variables_pointer->set_units(25, "pip"); variables_pointer->set_use(25, Variables::Input); variables_pointer->set_name(26, "27"); variables_pointer->set_units(26, "pip"); variables_pointer->set_use(26, Variables::Input); variables_pointer->set_name(27, "28"); variables_pointer->set_units(27, "pip"); variables_pointer->set_use(27, Variables::Input); variables_pointer->set_name(28, "29"); variables_pointer->set_units(28, "pip"); variables_pointer->set_use(28, Variables::Input); variables_pointer->set_name(29, "30"); variables_pointer->set_units(29, "pip"); variables_pointer->set_use(29, Variables::Input); variables_pointer->set_name(30, "31"); variables_pointer->set_units(30, "pip"); variables_pointer->set_use(30, Variables::Input); variables_pointer->set_name(31, "32"); variables_pointer->set_units(31, "pip"); variables_pointer->set_use(31, Variables::Input); variables_pointer->set_name(32, "33"); variables_pointer->set_units(32, "pip"); variables_pointer->set_use(32, Variables::Input); variables_pointer->set_name(33, "34"); variables_pointer->set_units(33, "pip"); variables_pointer->set_use(33, Variables::Input); variables_pointer->set_name(34, "35"); variables_pointer->set_units(34, "pip"); variables_pointer->set_use(34, Variables::Input); variables_pointer->set_name(35, "36"); variables_pointer->set_units(35, "pip"); variables_pointer->set_use(35, Variables::Input); variables_pointer->set_name(36, "37"); variables_pointer->set_units(36, "pip"); variables_pointer->set_use(36, Variables::Input); variables_pointer->set_name(37, "38"); variables_pointer->set_units(37, "pip"); variables_pointer->set_use(37, Variables::Input); variables_pointer->set_name(38, "39"); variables_pointer->set_units(38, "pip"); variables_pointer->set_use(38, Variables::Input); variables_pointer->set_name(39, "40"); variables_pointer->set_units(39, "pip"); variables_pointer->set_use(39, Variables::Input); variables_pointer->set_name(40, "41"); variables_pointer->set_units(40, "pip"); variables_pointer->set_use(40, Variables::Input); variables_pointer->set_name(41, "42"); variables_pointer->set_units(41, "pip"); variables_pointer->set_use(41, Variables::Input); variables_pointer->set_name(42, "43"); variables_pointer->set_units(42, "pip"); variables_pointer->set_use(42, Variables::Input); variables_pointer->set_name(43, "target"); variables_pointer->set_use(43, Variables::Target); const Matrix<std::string> inputs_information = variables_pointer->arrange_inputs_information(); const Matrix<std::string> targets_information = variables_pointer->arrange_targets_information(); // Instances Instances* instances_pointer = data_set.get_instances_pointer(); instances_pointer->split_random_indices(); //instances_pointer->split_random_indices(0.7, 0.15, 0.15); const Vector< Statistics<double> > inputs_statistics = data_set.scale_inputs_minimum_maximum(); // mapminmax const Vector< Statistics<double> > targets_statistics = data_set.scale_targets_minimum_maximum(); // Neural network const size_t inputs_number = variables_pointer->count_inputs_number(); const size_t hidden_perceptrons_number = 100; const size_t outputs_number = variables_pointer->count_targets_number(); neural_network.set(inputs_number, hidden_perceptrons_number, outputs_number); // quale funzione di attivazione? //NeuralNetwork neural_network(inputs_number, hidden_perceptrons_number, outputs_number); // validation ?? Inputs* inputs = neural_network.get_inputs_pointer(); inputs->set_information(inputs_information); Outputs* outputs = neural_network.get_outputs_pointer(); outputs->set_information(targets_information); neural_network.construct_scaling_layer(); ScalingLayer* scaling_layer_pointer = neural_network.get_scaling_layer_pointer(); scaling_layer_pointer->set_statistics(inputs_statistics); scaling_layer_pointer->set_scaling_method(ScalingLayer::NoScaling); neural_network.construct_unscaling_layer(); UnscalingLayer* unscaling_layer_pointer = neural_network.get_unscaling_layer_pointer(); unscaling_layer_pointer->set_statistics(targets_statistics); unscaling_layer_pointer->set_unscaling_method(UnscalingLayer::NoUnscaling); // Loss index loss_index.set_data_set_pointer(&data_set); loss_index.set_neural_network_pointer(&neural_network); // mse?? loss_index.set_regularization_type(LossIndex::NEURAL_PARAMETERS_NORM); // ?? // Training strategy object training_strategy.set(&loss_index); //TrainingStrategy training_strategy(&neural_network, &data_set); // non sono i dati // Error :OpenNN Exception: TrainingStrategy class. // LevenbergMarquardtAlgorithm* get_Levenberg_Marquardt_algorithm_pointer(void) const method. // Levenberg_Marquardt algorithm pointer is NULL. // ottenere il valore esatto del pointer es.:0.0000 // CASE 2 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// LevenbergMarquardtAlgorithm* Levenberg_Marquardt_algorithm_pointer = training_strategy.get_Levenberg_Marquardt_algorithm_pointer(); Levenberg_Marquardt_algorithm_pointer->set_maximum_iterations_number(1000); Levenberg_Marquardt_algorithm_pointer->set_display_period(10); Levenberg_Marquardt_algorithm_pointer->set_minimum_loss_increase(1.0e-6); Levenberg_Marquardt_algorithm_pointer ->set_reserve_loss_history(true); // CASE 1 /////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// QuasiNewtonMethod* quasi_Newton_method_pointer = training_strategy.get_quasi_Newton_method_pointer(); quasi_Newton_method_pointer->set_maximum_iterations_number(1000); quasi_Newton_method_pointer->set_display_period(10); quasi_Newton_method_pointer->set_minimum_loss_increase(1.0e-6); quasi_Newton_method_pointer->set_reserve_loss_history(true); // /////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// } #ifdef __OPENNN_MPI__ MPI_Barrier(MPI_COMM_WORLD); local_data_set.set_MPI(&data_set); local_neural_network.set_MPI(&neural_network); local_loss_index.set_MPI(&local_data_set,&local_neural_network,&loss_index); local_training_strategy.set_MPI(&local_loss_index,&training_strategy); MPI_Barrier(MPI_COMM_WORLD); TrainingStrategy::Results training_strategy_results = local_training_strategy.perform_training(); #else TrainingStrategy::Results training_strategy_results = training_strategy.perform_training(); #endif if(rank == 0) { #ifdef __OPENNN_MPI__ neural_network.set_multilayer_perceptron_pointer(local_neural_network.get_multilayer_perceptron_pointer()); #endif // Testing analysis TestingAnalysis testing_analysis(&neural_network, &data_set); TestingAnalysis::LinearRegressionResults linear_regression_results = testing_analysis.perform_linear_regression_analysis(); // Save results ScalingLayer* scaling_layer_pointer = neural_network.get_scaling_layer_pointer(); UnscalingLayer* unscaling_layer_pointer = neural_network.get_unscaling_layer_pointer(); scaling_layer_pointer->set_scaling_method(ScalingLayer::MinimumMaximum); unscaling_layer_pointer->set_unscaling_method(UnscalingLayer::MinimumMaximum); data_set.save("../data/data_set.xml"); neural_network.save("../data/neural_network.xml"); neural_network.save_expression("../data/expression.txt"); loss_index.save("../data/loss_index.xml"); training_strategy.save("../data/training_strategy.xml"); training_strategy_results.save("../data/training_strategy_results.dat"); linear_regression_results.save("../data/linear_regression_analysis_results.dat"); } #ifdef __OPENNN_MPI__ MPI_Barrier(MPI_COMM_WORLD); MPI_Finalize(); #endif return(0); } catch(std::exception& e) { std::cerr << e.what() << std::endl; std::getchar(); return(1); } } // OpenNN: Open Neural Networks Library. // Copyright (C) 2005-2015 Roberto Lopez. // // This library is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 2.1 of the License, or any later version. // // This library is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU // Lesser General Public License for more details. // You should have received a copy of the GNU Lesser General Public // License along with this library; if not, write to the Free Software // Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA[0_1552982666677_main.cpp](Uploading 100%)Thanks!
